Multi-objective land-use allocation considering landslide risk under climate change: Case study in pyeongchang-gun, Korea

Abstract

Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslide occurrences may increase under climate change, due to the increasing variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damage; however, planning in real life is a complex and nonlinear problem. For such multi-objective problems, genetic algorithms may be the most appropriate optimization tools. Therefore, in this study, we suggest a comprehensive land-use allocation plan using the Non-dominated Sorting Genetic Algorithm II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. Our study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in urban sprawl into the hazard zone where a large-scale landslide occurred in 2006. We obtain 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and second objectives. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation.